Adaptive Video Quality Throttling Based on Network Bandwidth for Virtual Classroom Systems

2015 ◽  
pp. 37-46 ◽  
Author(s):  
Jobina Mary Varghese ◽  
Balaji Hariharan ◽  
G. Uma ◽  
Ram Kumar
Keyword(s):  
Video Quality ◽  
Virtual Classroom ◽  
Network Bandwidth ◽  
Adaptive Video

Reference Viewpoints Selection for Multi-View Video Plus Depth Coding Based on the Network Bandwidth Constraint

2013 ◽  
Vol 303-306 ◽  
pp. 2134-2138
Author(s):  
Lei Luo ◽  
Rong Xin Jiang ◽  
Xiang Tian ◽  
Yao Wu Chen
Keyword(s):  
Video Quality ◽  
Server Side ◽  
Network Bandwidth ◽  
Bandwidth Constraint ◽  
Depth Coding ◽  
Selection For ◽  
Free Viewpoint Video ◽  
The Impact

In multi-view video plus depth (MVD) coding based free viewpoint video applications, a few reference viewpoints’ texture and depth videos should be compressed and transmitted at the server side. At the terminal side, the display view videos could be the decoded reference view videos or the virtual viewpoints’ videos which are synthesized by DIBR technology. The entire video quality of all display views are decided by the number of reference viewpoints and the compression distortion of each reference viewpoint’s texture and depth videos. This paper studies the impact of the reference viewpoints selection on the entire video quality of all display views. The results show that depending on the available network bandwidth, the MVD coding requires different selections of reference viewpoints to maximize the entire video quality of all display views.

scholarly journals On the trajectory of video quality transition in HTTP adaptive video streaming

2017 ◽  
Vol 24 (3) ◽  
pp. 327-340 ◽  
Author(s):  
Yusuf Sani ◽  
Andreas Mauthe ◽  
Christopher Edwards
Keyword(s):  
Video Streaming ◽  
Video Quality ◽  
Adaptive Video Streaming ◽  
Adaptive Video

scholarly journals Uplink vs. Downlink: Machine Learning-Based Quality Prediction for HTTP Adaptive Video Streaming

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4172
Author(s):  
Frank Loh ◽  
Fabian Poignée ◽  
Florian Wamser ◽  
Ferdinand Leidinger ◽  
Tobias Hoßfeld
Keyword(s):  
Machine Learning ◽  
Video Streaming ◽  
Video Quality ◽  
Mobile App ◽  
Learning Approaches ◽  
Data Set ◽  
Adaptive Video Streaming ◽  
Adaptive Video ◽  
Quality Video ◽  
Current Monitoring

Streaming video is responsible for the bulk of Internet traffic these days. For this reason, Internet providers and network operators try to make predictions and assessments about the streaming quality for an end user. Current monitoring solutions are based on a variety of different machine learning approaches. The challenge for providers and operators nowadays is that existing approaches require large amounts of data. In this work, the most relevant quality of experience metrics, i.e., the initial playback delay, the video streaming quality, video quality changes, and video rebuffering events, are examined using a voluminous data set of more than 13,000 YouTube video streaming runs that were collected with the native YouTube mobile app. Three Machine Learning models are developed and compared to estimate playback behavior based on uplink request information. The main focus has been on developing a lightweight approach using as few features and as little data as possible, while maintaining state-of-the-art performance.

scholarly journals Fuzzy Logic-Based Adaptive Point Cloud Video Streaming

2020 ◽  
Author(s):  
Zhi Liu ◽  
Jie Li ◽  
Xianfu Chen ◽  
Celimuge Wu ◽  
susumu ishihara ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Video Streaming ◽  
Point Cloud ◽  
Degrees Of Freedom ◽  
Video Quality ◽  
Base Layer ◽  
Quality Level ◽  
Real Point ◽  
Network Bandwidth ◽  
Video Streaming Scheme

Point cloud video provides 6 degrees of freedom (6DoF) viewing experiences to allow users to freely select the viewing angles of 3D scenes and is expected to be the next-generation video. This paper studies the point cloud video streaming and proposes a fuzzy logic-based point cloud video streaming scheme to solve the inherent technical issues. In particular, a point cloud video is first partitioned into smaller tiles, along with a low-quality base layer covering the entire video. Each tile is encoded into different quality levels, and both the compressed and uncompressed (i.e., decoded) versions of each tile are prepared for selection. Then, based on the user’s viewing angle and predicted future network bandwidth condition, fuzzy logic empowered quality level selection, with properly defined novel fuzzification, fuzzy rules, and defuzzification, is conducted to maximize the received point cloud video quality under the communication resource, computational resource and quality requirements constraints. Extensive simulations based on real point cloud video sequences and network traces are conducted, and the results reveal the superiority of the proposed scheme over the baseline scheme. To the best of our knowledge, this is the first work studying point cloud video streaming using fuzzy logic. <br>

scholarly journals Optimal Multi-Interface Selection for Mobile Video Streaming in Efficient Battery Consumption and Data Usage

2016 ◽  
Vol 2016 ◽  
pp. 1-18 ◽  
Author(s):  
Seonghoon Moon ◽  
Juwan Yoo ◽  
Songkuk Kim
Keyword(s):  
High Resolution ◽  
High Performance ◽  
Scalable Video Coding ◽  
Video Quality ◽  
Battery Life ◽  
Limited Data ◽  
Binary Integer Programming ◽  
Network Bandwidth ◽  
Data Usage ◽  
Data Constraints

With the proliferation of high-performance, large-screen mobile devices, users’ expectations of having access to high-resolution video content in smooth network environments are steadily growing. To guarantee such stable streaming, a high cellular network bandwidth is required; yet network providers often charge high prices for even limited data plans. Moreover, the costs of smoothly streaming high-resolution videos are not merely monetary; the device’s battery life must also be accounted for. To resolve these problems, we design an optimal multi-interface selection system for streaming video over HTTP/TCP. An optimization problem including battery life and LTE data constraints is derived and then solved using binary integer programming. Additionally, the system is designed with an adoption of split-layer scalable video coding, which provides direct adaptations of video quality and prevents out-of-order packet delivery problems. The proposed system is evaluated using a prototype application in a real, iOS-based device as well as through experiments conducted in heterogeneous mobile scenarios. Results show that the system not only guarantees the highest-possible video quality, but also prevents reckless consumption of LTE data and battery life.

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